brm unit 2&3 notes

7/28/2019 Brm Unit 2&3 Notes

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UNIT II&III NOTES

1. A true experimental design is one in which the researcher manipulates the IndependentVariable (or variables) to observe its effect on some behavior.

2. Any variable that is not considered as an independent variable in the model but thatinfluences the dependent variable (outside form the models purview) is an exogenousvariable.

3. Primary data is data gathered for the first time by the researcher; secondary data is data

taken by the researcher from secondary sources, internal or external.

4. A scale is basically a continuous spectrum or series of categories and has been defined as

any series of items that are arranged progressively according to value or magnitude, into

which an item can be placed according to its quantification

5. Reliability refers to the consistency and stability of a measure. A test is consideredreliable if the researcher gets the same result repeatedly. Types of reliability: 1. Test

retest method 2. Parallel form method 3. Split half method, 4. Inter item consistency

method.

6. Validity: Validity is the extent to which a test measures what it claims to measure. It is

vital for a test to be valid in order for the results to be accurately applied and interpreted .

7. In an interval scale the distance between the measure points are assumed to be equal or

equidistant and a ratio scale is the top level of measurement and have a true zero. The

data obtained from these scales will have adequate validity with minimum errors.Whenusing these scales all parametric tests and other modeling techniques can be used.

8. Criterion validity is a measure of how well one variable or set of variables predicts anoutcome based on information from other variables.

9. A fo cus

group is a

form ofqualitative

research inwhich a group of people are asked about their perceptions, opinions, beliefs, and attitudes

towards a product or towards any issues or problems.

10. The target population for a survey is the entire set of units for which the survey data areto be used to make inferences. Ex if the research objective is to study old people who are

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in the age group of 65-70 years spending pattern in a particular region then the target

population should be all the people in that age group in that particular area.

11. Case Study: This study involves intensive study of a relatively small number of cases. In

this method, much emphasis is on obtaining a complete description and understanding of

factors in each case, regardless of the number involved.

12. Measurement is defined as the assignment of numbers to characteristics of objects or

events according to rules.

13. Nominal scale are categorical scales used to identify, label or categorise objects or

persons or events

14. Ordinal scale is a ranking scale that indicates ordered relationship among the objects or

events. It involves assigning numbers to objects to indicate the relative extent to which

the objects possess some characteristic.

15. Interval scale is otherwise called as rating scale. It involves the use of numbers to rate

objects or events. It interval scales, numerically equal distances on the scale representequal values in the characteristic being measured.

16. Ratio scales differ from interval scales in that it has a natural/absolute zero. It possesses

all the properties of the normal, ordinal and interval scales.

17. Paired comparison scaling as its name indicates involves presentation of two objects

and asking the respondents to select one according to some criteria. The data are obtainedusing ordinal scale. For example, a respondent may be asked to indicate his/her

preference for TVs in a paired manner.

18. Rank order scaling: This is another popular comparative scaling technique. In rankorder scaling is done by presenting the respondents with several objects simultaneously

and asked to order or rank them based on a particular criterion.

19. Non-comparative scales or otherwise called as nomadic scales because only one object

is evaluated at a time.

20. Semantic differential scale is a popular scaling technique next to Likert scale. In this

scale, the respondents associate their response with bipolar labels that have semantic

meaning. The respondents rate objects on a number of itemized, seven point rating scalesbounded at each end by one of two bipolar adjectives such as Excellent and Very

bad.

21. A questionnaire is defined as a formalized schedule for collecting data fromrespondents. It may be called as a schedule, interview form or measuring instrument.

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DESCRIPTIVE ANSWERS

Types of Measurement Scale and Rating scales

Nominal:

The lowest measurement level you can use, from a statistical point of view, is a nominal

scale.A nominal scale, as the name implies, is simply some placing of data intocategories, without any order or structure.In research activities a YES/NO scale is

nominal. It has no order and there is no distance between YES and NO.

The statistics which can be used with nominal scales are in the non-parametric group.

The most likely ones would be:

modecrosstabulation - with chi-square

Ordinal:

An ordinal scale is next up the list in terms of power of measurement. The simplest

ordinal scale is a ranking. When a market researcher asks you to rank 5 types of colas

from most flavorful to least flavourful, he/she is asking you to create an ordinal scale ofpreference. There is no objective distance between any two points on your subjective

scale. For you the top beer may be far superior to the second preferred beer but, to

another respondent with the same top and second beer, the distance may be subjectively

small. An ordinal scale only lets you interpret gross order and not the relative positionaldistances.

Ordinal data would use non-parametric statistics. These would include:

Median and mode

rank order correlationnon-parametric analysis of variance

Interval:

The standard survey rating scale is an interval scale.When you are asked to rate your satisfaction

with a piece of software on a 7 point scale, from Dissatisfied to Satisfied, you are using an

interval scale.

It is an interval scale because it is assumed to have equidistant points between each of the scaleelements. This means that we can interpret differences in the distance along the scale. We

contrast this to an ordinal scale where we can only talk about differences in order, not differences

in the degree of order.

Interval scales are also scales which are defined by metrics such as logarithms. In these cases, thedistances are note equal but they are strictly definable based on the metric used.

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Interval scale data would use parametric statistical techniques:

Mean and standard deviation

Correlation - r

RegressionAnalysis of variance

Factor analysis

Plus a whole range of advanced multivariate and modelling techniques

Ratio:

A ratio scale is the top level of measurement and is not often available in social research. The

factor which clearly defines a ratio scale is that it has a true zero point.The simplest example of a

ratio scale is the measurement of length

All type of parametric tests and other modelings can be applied

Types of Rating Scales:

Measurement scales that allow a respondent to register the degree (or amount) of a characteristic

or attribute possessed by an object directly on the scale.

Six main types of rating scales:

1. Category scale

2. Semantic differential scale3. Stapel scale

4. Likert scale (Summated ratings scale)

5. Constant sum scale6. Graphic scale

1. Category Scale

A rating scale which the response options provided for a closed-ended question arelabeled with specific verbal descriptions.

Example:

Please rate car model A on each of the following dimensions:

Poor Fair Good V.goodExcellent

a)Durability [ ] [ ] [ ] [ ][ ]

b)Fuel consumption [ ] [ ] [ ] [ ][ ]

Characteristics:1. Response options are still verbal descriptions.

2. Response categories are usually ordered according to a particular descriptive orevaluative dimension.

3. Therefore scale has ordinal properties.

4. However, researchers often assume that it possesses interval properties => but this isonly an assumption.

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** One special version is the Simple category scale.

Simple Category Scale

A category scale with only two response categories (or scale points) both of which arelabeled.

Example:

Please rate brand A on each of the following dimensions:

poor excellent

a) Durability [ ] [ ]

b) Fuel consumption [ ] [ ]

2. Semantic Differential Scale

A rating scale in which bipolar adjectives are placed at both ends (or poles) of the scale,

and response options are expressed as semantic space.Example:

Please rate car model A on each of the following dimensions:

Durable ---:-X-:---:---:---:---:--- Not durable

Low fuel consumption ---:---:---:---:---:-X-:--- High fuel consumption

Characteristics

1. The scale has properties of an interval scale.2. Sometimes descriptive phrases are used instead of bipolar adjectives, especially when it

is difficult to get adjectives that are exact opposites

3. It is often used to construct an image profile.

3.Stapel Scale

A simplified version of the semantic differential scale in which a single adjective or

descriptive phrase is used instead of bipolar adjectives.

Characteristics1. The scale measures both the direction and intensity of the attribute

simultaneously.

2. It has properties similar to the semantic differential.

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Example:

4. Constant-Sum Scale

A rating scale in which respondents divide a constant sum among different attributes ofan object (usually to indicate the relative importance of each attribute).Assumed tohave ratio level properties.

Example: Divide 100 points among the following dimensions to indicate their level of

importance to you when you purchase a car:

Durability

Fuel Consumption

Total 100

5. Numerical Scale

Any rating scale in which numbers rather than semantic space or verbal descriptions are

used as response options.

Examples:

Poor Excellent

Durability 1 2 3 4 5 6 7

Durable 1 2 3 4 5 6 7 Not durable

6.Graphic Ratings Scales

Rating scales in which respondents rate an object on a graphic continuum, usually a

straight line. Modified versions are the ladder scale and happy face scale.

Characteristics1. The straight line scale has ratio level properties.

2. The ladder and happy face scales have properties depending on the labeling

option chosen whether all response categories are labeled (ordinal properties)or only the scale end-points are labeled (interval properties).

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Model A

-3 -2 -1 Durable Car 1 2 3

-3 -2 -1 Good Fuel Conaumption 1 2 3


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7.The Likert Scale (Summated Ratings Scale)

A multiple item rating scale in which the degree of an attribute possessed by an object isdetermined by asking respondents to agree or disagree with a series of positive and/or

negative statements describing the object.

Example: Characteristicsof the Likert Scale1. The following procedure is used to analyze data from Likert scales:2. First, weights are assigned to the responses options, e.g. Totally agree=1,

Agree=2, etc

3. Then negatively-worded statements are reverse-coded (or reverse scored). E.g.

a score of 2 for a negatively-worded statement with a 5-point response optionsis equivalent to a score of 4 on an equivalent positive statement.

4. Next, scores are summed across statements to arrive at a total (or summated)

score.5. Each respondents score can then be compared with the mean score or the

scores of other respondents to determine his level of attitude, loyalty, or otherconstruct that is being measured

6. Note that the response for each individual statement is expressed on a category

scale.

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Issues In Selecting A Measurement Scale

Whether to use single or index measure.

Whether to use a ranking, sorting, choice, or rating scale.

Whether to use monadic or comparative scale.

Monadic rating scale is one in which respondents evaluate an object inisolation

Comparative scale s one in which the object is evaluated in relation to other objects

Construction and labeling is different for monadic and comparative scales

Whether to use category labels or not.

If the decision is to use category labels, what labels to use.

Number of response options (scale categories) to use, i.e whether to use 2, 3, 4, 5, etc

response categories

In general, the larger the number of categories the more sensitive the scale is; but also the

more difficult it is for respondents to answer

Whether to use balanced or unbalanced scale.

A balanced scale has an equal number of points to the left and right of a mid-point. An

unbalanced scale has more response options on one side than the other Whether the scale should force choice among the response categories, i.e should the scale

contain a neutral or dont know category.

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Types of Experimental Designs

PRE-EXPERIMENTAL DESIGN

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After only Design with one EGBefore after design with one EG

After only design with one EG & One CG

TRUE EXPERIMENTAL DESIGN

Pre test- post test control group

Post-test only control groupSolomon four group design

STATISTICAL DESIGN

Completely Randomized DesignRandomised Block Design

Factorial Design

Latin Square Design

WRITE THE CONTENTS FROM THE CLASS POWERPOINT

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Techniques of Primary Data collection method:

1. ObservationNatural versus Contrived observation

Disguised versus non-disguised observation

Other Types

2. Questionnaire:Structured vs unstructured

Disguised vs non-disguised

WRITE THE CONTENTS FROM THE CLASS POWERPOINT

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TYPES OF SAMPLING METHODS:

Probability Samples

The idea behind this type is random selection. More specifically, each sample from the population

of interest has a known probability of selection under a given sampling scheme. There

are four categories of probability samples described below.

Simple Random Sampling

The most widely known type of a random sample is the simple random sample (SRS). This ischaracterized by the fact that the probability of selection is the same for every case in the

population. Simple random sampling is a method of selecting n units from a population of size Nsuch that every possible sample of size n has equal chance of being drawn.

An example may make this easier to understand. Imagine you want to carry out a survey of 100

voters in a small town with a population of 1,000 eligible voters. With a town this size, there are

"old-fashioned" ways to draw a sample. For example, we could write the names of all voters on apiece of paper, put all pieces of paper into a box and draw 100 tickets at random. You shake the

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box, draw a piece of paper and set it aside, shake again, draw another, set it aside, etc. until wehad 100 slips of paper. These 100 form our sample. And this sample would be drawn through a

simple random sampling procedure - at each draw, every name in the box had the same

probability of being chosen.

Systematic Sampling

This method of sampling is at first glance very different from SRS. In practice, it is a variant ofsimple random sampling that involves some listing of elements - every nth element of list is then

drawn for inclusion in the sample. Say you have a list of 10,000 people and you want a sample of

1,000.Creating such a sample includes three steps:

1. Divide number of cases in the population by the desired sample size. In this example,

dividing 10,000 by 1,000 gives a value of 10.

2. Select a random number between one and the value attained in Step 1. In this example, wechoose a number between 1 and 10 - say we pick 7.

3. Starting with case number chosen in Step 2, take every tenth record (7, 17, 27, etc.).

More generally, suppose that the N units in the population are ranked 1 to N in some order (e.g.,alphabetic). To select a sample of n units, we take a unit at random, from the 1st k units and take

every k-th unit thereafter.

The advantages of systematic sampling method over simple random sampling include:

It is easier to draw a sample and often easier to execute without mistakes. This is a particular

advantage when the drawing is done in the field.

Intuitively, you might think that systematic sampling might be more precise than SRS. In effect it

stratifies the population into n strata, consisting of the 1st k units, the 2nd k units, and so on. Thus,we might expect the systematic sample to be as precise as a stratified random sample with one

unit per stratum. The difference is that with the systematic one the units occur at the same relative

position in the stratum whereas with the stratified, the position in the stratum is determinedseparately by randomization within each stratum

Stratified Random Sampling

In this form of sampling, the population is first divided into two or more mutually exclusive

segments based on some categories of variables of interest in the research. It is designed to

organize the population into homogenous subsets before sampling, then drawing a random samplewithin each subset. With stratified random sampling the population of N units is divided into

subpopulations of units respectively. These subpopulations, calledstrata, are non-overlapping and

together they comprise the whole of the population. When these have been determined, a sample

is drawn from each, with a separate draw for each of the different strata. The sample sizes withinthe strata are denoted by respectively. If a SRS is taken within each stratum, then the whole

sampling procedure is described as stratified random sampling.

Stratification is a common technique. There are many reasons for this, such as:

If data of known precision are wanted for certain subpopulations, than each of these should betreated as a population in its own right.

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Administrative convenience may dictate the use of stratification, for example, if an agency

administering a survey may have regional offices, which can supervise the survey for a part of the

population.

Sampling problems may be inherent with certain sub populations, such as people living in

institutions (e.g. hotels, hospitals).

Stratification may improve the estimates of characteristics of the whole population. It may be

possible to divide a heterogeneous population into sub-populations, each of which is internallyhomogenous. If these strata are homogenous, i.e., the measurements vary little from one unit to

another; a precise estimate of any stratum mean can be obtained from a small sample in that

stratum. The estimate can then be combined into a precise estimate for the whole population.

There is also a statistical advantage in the method, as a stratified random sample nearly alwaysresults in a smaller variance for the estimated mean or other population parameters of interest.

Cluster Sampling:

In some instances the sampling unit consists of a group or cluster of smaller units that we callelements or subunits (these are the units of analysis for your study). There are two main reasonsfor the widespread application of cluster sampling. Although the first intention may be to use the

elements as sampling units, it is found in many surveys that no reliable list of elements in the

population is available and that it would be prohibitively expensive to construct such a list. In

many countries there are no complete and updated lists of the people, the houses or the farms inany large geographical region.

The below diagram is for understanding purpose only do not draw in examination

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Important things about cluster sampling:

Most large scale surveys are done using cluster sampling;

Clustering may be combined with stratification, typically by clustering within strata;In general, for a given sample size n cluster samples are less accurate than the other types of

sampling in the sense that the parameters you estimate will have greater variability than

an SRS, stratified random or systematic sample.

Nonprobability Sampling

Social research is often conducted in situations where a researcher cannot select the kinds of

probability samples used in large-scale social surveys. For example, say you wanted to studyhomelessness - there is no list of homeless individuals nor are you likely to create such a list.

However, you need to get some kind of a sample of respondents in order to conduct your research.

To gather such a sample, you would likely use some form of non-probability sampling.

To reiterate, the primary difference between probability methods of sampling and non-probability

methods is that in the latter you do not know the likelihood that any element of a population willbe selected for study.

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There are four primary types of non-probability sampling methods:

Availability Sampling

Availability sampling is a method of choosing subjects who are available or easy to find. This

method is also sometimes referred to as haphazard, accidental, or convenience sampling. The

primary advantage of the method is that it is very easy to carry out, relative to other methods. Aresearcher can merely stand out on his/her favourite street corner or in his/her favorite tavern and

hand out surveys. One place this used to show up often is in university courses. Years ago,

researchers often would conduct surveys of students in their large lecture courses

. For example, all students taking introductory sociology courses would have been given a survey

and compelled to fill it out. There are some advantages to this design - it is easy to do, particularly

with a captive audience, and in some schools you can attain a large number of interviews throughthis method.

The primary problem with availability sampling is that you can never be certain what population

the participants in the study represent. The population is unknown, the method for selecting casesis haphazard, and the cases studied probably don't represent any population you could come up

with.However, there are some situations in which this kind of design has advantages - for example,survey designers often want to have some people respond to their survey before it is given out in

the "real" research setting as a way of making certain the questions make sense to respondents.

For this purpose, availability sampling is not a bad way to get a group to take a survey, though in

this case researchers care less about the specific responses given than whether the instrument isconfusing or makes people feel bad.

Quota Sampling

Quota sampling is designed to overcome the most obvious flaw of availability sampling. Rather

than taking just anyone, you set quotas to ensure that the sample you get represents certain

haracteristics in proportion to their prevalence in the population. Note that for this method, youhave to know something about the characteristics of the population ahead of time. Say you want

to make sure you have a sample proportional to the population in terms of gender - you have to

know what percentage of the population is male and female, then collect sample until yours

matches. Marketing studies are particularly fond of this form of research design.

The primary problem with this form of sampling is that even when we know that a quota sample

is representative of the particular characteristics for which quotas have been set, we have no wayof knowing if sample is representative in terms of any other characteristics. If we set quotas for

gender and age, we are likely to attain a sample with good representativeness on age and gender,

but one that may not be very representative in terms of income and education or other factors.

Moreover, because researchers can set quotas for only a small fraction of the characteristics

relevant to a study quota sampling is really not much better than availability sampling. Toreiterate, you must know the characteristics of the entire population to set quotas; otherwise

there's not much point to setting up quotas. Finally, interviewers often introduce bias when

allowed to self-select respondents, which is usually the case in this form of research. In choosing

males 18-25, interviewers are more likely to choose those that are better-dressed, seem more

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Instructions1. Instructions on how to answer should be communicated to the respondent as part

of the question.

2. Special instructions to the interviewer should be clear and located on the questionnaire.

3. Clearly distinguish the instructions to the interviewer questions and responses by always

putting the instructions to the interviewer CAPS,Italics, etc.

Content1. Write brief questions.

2. Have a specific goal for each question.

3. Make efforts to write questions that are valid measures of the studys variables.

4. Be careful not to assume behavior or knowledge on the part of any respondent.

5. If a question contains facts, make sure they are accurate.

6. Carefully choose wording so that accurate information is collected.

Be precise and specific in the use of concepts (for example, government--is it city,

county, or federal?).

Be precise and specific regarding time, either as a period of recall or as a time limitto a certain behavior.

Avoid "loaded" questions that suggest to respondents that one answer is preferable to

another.

Avoid double questions where two or more issues are mentioned.

Avoid all-inclusive terms such as "never" or "always."

Avoid the use of technical terms and abbreviations that can be misconstrued.

Avoid the use of inflammatory words such as "racist" or "exploitation."

7. Be sure all questions are relevant to the research goal.

8. Be certain all important questions are asked.

Response Categories

1. Response categories must match the attributes mentioned in question.

2. Response categories to closed-ended items must be:

sufficiently exhaustive.

mutually exclusive.

the categories respondents would naturally use to classify the item or themselves.

3. Questions may contain a response category of "don't know" or "no answer/refusal."

Threats to internal validity

Write the contents from the power point given

Problems

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Try to solve the slef assessment problems given in the power point slides

Sampling slides

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brm unit 2&3 notes

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